The ERS Scatterometer Wind Measurement Accuracy: Evidence of Seasonal and Regional Biases

Abstract

A validation of European Space Agency (ESA) remote sensing satellite (ERS) scatterometer ocean wind measurements is performed using a formalism recently proposed for and applied to NASA scatterometer (NSCAT) and Special Sensor Microwave Imager (SSM/I) measurements. This simple analytical model relates scatterometer measurements to true winds, taking into account errors in the satellite winds as well as errors in the data used for reference. In this study, National Data Buoy Center (NDBC) buoy winds are the chosen reference. In addition, ECMWF analysis winds are used as a third data source to completely determine the errors via a triple collocation analysis. According to this development, the resulting wind speed error analysis indicates that ERS scatterometer estimates are negatively biased at light winds. This result differs from recent results determined using standard regression analysis. It is also shown that ERS and NSCAT measurement accuracies are comparable in an overall sense. This error model provides a more certain measure of both random and systematic terms and the authors use this tool to look at possible systematic scatterometer wind speed biases in two separate long-term (1992?98) ERS datasets. The chosen approach examines temporal and spatial variation between ocean buoy and ERS-derived winds to identify both seasonal and regional ERS wind error signatures. First, data indicate a time-dependent bias between NDBC and ERS winds that is strongly correlated with the seasonal cycle. Buoy-derived long-wave and atmospheric stability parameter averages exhibit similar cycles and are the likely geophysical links to this scatterometer error. An illustration of regional or spatially varying error sources is further provided using ERS data collocated with Tropical Atmosphere and Ocean (TAO) buoy array measurements. In this case the long-term average wind speed bias between TAO and ERS exhibits well-defined spatial structures within the equatorial belt (10°N, 10°S). Bias variations show qualitative agreement with a near-surface current climatology map for this Pacific region and also with the limited available buoy current measurements. Overall results indicate small but systematic nonwind sea surface effects on scatterometer products. It is concluded that there cannot be one set of values for ERS scatterometer wind validation parameters. Accounting for surface effects on scatterometer measurements may need consideration to ensure proper assimilation of scatterometer data into weather forecasting and climate prediction models.